Abstract
Constraint satisfaction has been applied with great success in closed-world scenarios, where all options and constraints are known from the beginning and fixed. With the internet, many of the traditional CSP applications in resource allocation, scheduling and planning pose themselves in open-world settings, where options and constraints must be gathered from different agents in a network. We define open constraint optimization as a model of such tasks.
Under the assumption that options are discovered in decreasing order of preference, it becomes possible to guarantee optimality even when domains and constraints are not completely known. We propose several algorithms for solving open constraint optimization problems by incrementally gathering options through the network. We report empirical results on their performance on random problems, and analyze how to achieve optimality with a minimal number of queries to the information sources.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cucchiara, R., Gavanelli, M., Lamma, E., Mello, P., Milano, M., Piccardi, M.: Constraint propagation and value acquisition: why we should do it interactively. In: Proceedings of the 16th IJCAI, pp. 468–477. Morgan Kaufmann, San Francisco (1999)
Faltings, B., Macho-Gonzalez, S.: Open Constraint Satisfaction. In: Van Hentenryck, P. (ed.) CP 2002. LNCS, vol. 2470, pp. 356–370. Springer, Heidelberg (2002)
Bessière, C.: Arc-Consistency in Dynamic Constraint Satisfaction Problems. In: Proceedings of the 9th National Conference of the AAAI, pp. 221-226 (1991)
Yokoo, M.: Algorithms for Distributed Constraint Satisfaction: A Review. Autonomous Agents and Multi-Agent Systems 3(2), 189–212 (2000)
Modi, P.J., Shen, W.-M., Tambe, M., Yokoo, M.: An Asynchronous Complete Method for Distributed Constraint Optimization. Autonomous Agents and Multi-Agent Systems (September 2003)
Rossi, F., Petrie, C., Dhar, V.: On the equivalence of constraint satisfaction problems. In: Proceedings of ECAI 1990, pp. 550-556 (1990)
Stergiou, K., Walsh, T.: Encodings of Non-Binary Constraint Satisfaction Problems. In: Proceedings of the AAAI 1999, pp. 163-168 (1999)
Larrosa, X., Dechter, R.: On dual encodings for non-binary constraint satisfaction problems. In: Larrosa, X., Dechter, R. (eds.) CP 2000. LNCS, vol. 1894, p. 531. Springer, Heidelberg (2000)
Conen, W., Sandholm, T.: Partial-revelation VCG Mechanism for Combinatorial Auctions. In: Proceedings of the AAAI 2002, pp. 367-372 (2002)
Verfaillie, G., Lemaitre, M., Schiex, T.: Russian Doll Search for Solving Constraint Optimization Problems. In: Proc. of the 13th National Conference on Artificial Intelligence (AAAI 1996), Portland, OR, USA, pp. 181–187 (1996)
Korf, R.E.: Depth-first iterative deepening: an optimal admissible tree search. Artificial Intelligence 27(1), 97–109 (1985)
Ephrati, E., Rosenschein, J.S.: The Clarke tax as a consensus mechanism among automated agents. In: Proceedings of the 9th National Conference on Artificial Intelligence, San Jose, California, July 1991, pp. 173–178 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Faltings, B., Macho-Gonzalez, S. (2003). Open Constraint Optimization. In: Rossi, F. (eds) Principles and Practice of Constraint Programming – CP 2003. CP 2003. Lecture Notes in Computer Science, vol 2833. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45193-8_21
Download citation
DOI: https://doi.org/10.1007/978-3-540-45193-8_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20202-8
Online ISBN: 978-3-540-45193-8
eBook Packages: Springer Book Archive